<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">W. Maass</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">B. Cooper</style></author><author><style face="normal" font="default" size="100%">A. Sorbi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Motivation, theory, and applications of liquid state machines</style></title><secondary-title><style face="normal" font="default" size="100%">Computability in Context: Computation and Logic in the Real World</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Imperial College Press</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Liquid State Machine (LSM) has emerged as a computational model that is   more adequate than the Turing machine for describing computations in   biological networks of neurons. Characteristic features of this new model are   (i) that it is a model for adaptive computational systems, (ii) that it   provides a method for employing randomly connected circuits, or eve &amp;quot;found&amp;quot;   physical objects for meaningful computations, (iii) that it provides a   theoretical context where heterogeneous, rather than stereotypical, local   gates or processors increase the computational power of a circuit, (iv) that   it provides a method for multiplexing different computations (on a common   input) within the same circuit. This chapter reviews the motivation for this   model, its theoretical background, and current work on implementations of   this model in innovative artificial computing devices.&lt;/p&gt;</style></abstract></record></records></xml>